Evolvable block-based neural networks for classification of driver drowsiness based on heart rate variability

Abstract

Studies have shown that driver drowsiness is one of the main causes of road accidents. It is estimated that 30% of road accidents are caused by driver drowsiness, which creates a need for driver drowsiness detection in modern vehicle systems. Previous works have shown the viability of using heart rate variability (HRV) for detecting the onset of driver drowsiness. HRV is obtained for electrocardiogram (ECG) signals, of which the power bands can be analysed to determine the physiological state of a person. This paper introduces a new method to detect driver drowsiness by classifying the power spectrum of a person's HRV data using Block-based Neural Networks (BbNN), which is evolved using Genetic Algorithm (GA). For most cases, regular Artificial Neural Networks (ANN) are not suitable for high speed and efficient hardware implementation. BbNNs are better candidates due to its regular block based structure, has relatively fast computational speeds, lower resource consumption, and equal classifying strength in comparison to other ANNs. Preliminary work has shown promising results with up to 99.99% classification accuracy using the proposed BbNN detection system for HRV data.